Pros and cons: BigQuery Connector for SAP vs Cloud Data Fusion

[Dovile]

Considering options for ingesting SAP ERP data into BigQuery.  Plan is to ingest raw data and then process as needed (so ELT pattern). Two main options are:

1. BigQuery Connector for SAP
2. Cloud Data Fusion

What are pros and cons of each? Especially financial ($$$) ones? When is best to use which approach? Main concern – if we use CDC option, would this result in large BQ ingestion costs? Mainly because BigQuery Connector for SAP uses the BigQuery streaming API.

[ms4446]

BigQuery Connector for SAP:

Pros:

• Ease of Use: The BigQuery Connector for SAP is a fully managed service, making it straightforward to set up and use.
• Performance: Utilizes the BigQuery streaming API, ensuring efficient data ingestion into BigQuery.

Cons:

• Cost Implications: Streaming data into BigQuery can be more expensive than batch loading, especially with large volumes of data.
• Limited Features: Primarily designed for data ingestion from SAP to BigQuery, it might not support extensive ETL operations.
• Lack of Transformation Capabilities: There are no built-in transformation features. Any data transformation would require an external tool or process.

Cloud Data Fusion:

Pros:

• Wide Range of Features: Supports various features, including full and delta loads, change data capture (CDC), and data transformation.
• Flexibility: Offers more flexibility in how you ingest and process your SAP data.
• Scalability: Can handle demanding data workloads, scaling as needed.

Cons:

• Complexity: While powerful, Cloud Data Fusion might require more time and expertise to set up and manage compared to the BigQuery Connector for SAP.
• Cost Variability: Costs can vary based on the number of Data Fusion units used and the volume of data processed. It's essential to analyze expected data volumes and usage patterns to estimate costs.

Financial Considerations:

The costs associated with both the BigQuery Connector for SAP and Cloud Data Fusion depend on various factors, including data volume, ingestion frequency, and utilized features. While the BigQuery Connector might have higher costs for streaming large datasets, Cloud Data Fusion's costs are influenced by the complexity and volume of the data processing tasks.

When to Use Which:

Your choice between the two will hinge on your specific requirements:

• BigQuery Connector for SAP: Ideal for straightforward, real-time data ingestion from SAP to BigQuery. However, be mindful of potential costs with high-volume streaming.

• Cloud Data Fusion: Suited for scenarios requiring more complex data integration, transformation, or ingestion from multiple sources. While it offers more features and flexibility, it might come with a steeper learning curve and potentially higher costs, depending on usage.

CDC and BigQuery Ingestion Costs:

Using CDC can be cost-effective if the volume of change data is relatively small compared to the entire dataset. While CDC captures all changes, its cost-effectiveness versus full loads will depend on the specific scenario. The real-time insights provided by CDC can be invaluable, but it's crucial to weigh these benefits against potential costs.

Conclusion:

Both the BigQuery Connector for SAP and Cloud Data Fusion offer robust solutions for ingesting SAP ERP data into BigQuery. Your decision should factor in your specific needs, budget, and data volume. If real-time data ingestion is a priority, the BigQuery Connector is a strong contender. However, if you require more extensive data integration and transformation capabilities, Cloud Data Fusion might be the better choice. Always consider potential costs and benefits before finalizing your decision.